Integrating Conversational AI Models into Riva
While the TAO Toolkit is an excellent resource to train and finetune models, Riva provides resources to deploy those models into scalable services running on GPUs.
Along with the TAO conversational AI package, we provide the following sample resources on NGC to capture the end to end workflow of training a model with TAO and deploying them to Riva.
Conversational AI Task |
Jupyter Notebooks |
|---|---|
| Speech to Text | Speech to Text Notebook |
| Speech to Text Citrinet | Speech to Text Citrinet Notebook |
| Question Answering | Question Answering Notebook |
| Text Classification | Text Classification Notebook |
| Token Classification | Token Classification Notebook |
| Punctuation and Capitalization | Punctuation Capitalization Notebook |
| Intent and Slot Classification | Intent Slot Classification Notebook |
| NGram LM Notebook | NGram LM Notebook |
| Text to Speech | Text to Speech Notebook |
Each sample resource contain 2 sample notebooks,
To train the respective model using TAO Toolkit and generate an exported
.rivaTo use this exported
.rivafile and deploy it to Riva.
You may find more information about the same in the Riva Documentation.